{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_wine\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "outputs": [
    {
     "data": {
      "text/plain": "   粉丝数  关注数  性别  微博数  注册年限  互动数  视频累积播放量  TOP1  TOP2  TOP3  ...  综艺节目  旅游出行  \\\n0   71  276   0  655    11  109        0    29    20    14  ...     1     1   \n1  256  313   0  432    12  608     4051    29    20     5  ...     1     1   \n\n   财经  健康医疗  电视剧  EI  NS  TF  JP  MBTI类型  \n0   0     1    1   1   1   1   0    ISFJ  \n1   0     0    1   1   1   1   0    ISFJ  \n\n[2 rows x 52 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>粉丝数</th>\n      <th>关注数</th>\n      <th>性别</th>\n      <th>微博数</th>\n      <th>注册年限</th>\n      <th>互动数</th>\n      <th>视频累积播放量</th>\n      <th>TOP1</th>\n      <th>TOP2</th>\n      <th>TOP3</th>\n      <th>...</th>\n      <th>综艺节目</th>\n      <th>旅游出行</th>\n      <th>财经</th>\n      <th>健康医疗</th>\n      <th>电视剧</th>\n      <th>EI</th>\n      <th>NS</th>\n      <th>TF</th>\n      <th>JP</th>\n      <th>MBTI类型</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>71</td>\n      <td>276</td>\n      <td>0</td>\n      <td>655</td>\n      <td>11</td>\n      <td>109</td>\n      <td>0</td>\n      <td>29</td>\n      <td>20</td>\n      <td>14</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>ISFJ</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>256</td>\n      <td>313</td>\n      <td>0</td>\n      <td>432</td>\n      <td>12</td>\n      <td>608</td>\n      <td>4051</td>\n      <td>29</td>\n      <td>20</td>\n      <td>5</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>ISFJ</td>\n    </tr>\n  </tbody>\n</table>\n<p>2 rows × 52 columns</p>\n</div>"
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mbti_weibo_data = pd.read_csv('../data/mbti_weibo_data3.csv')\n",
    "mbti_weibo_data.head(2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "outputs": [],
   "source": [
    "baseFeatures = mbti_weibo_data.iloc[:,0:47]\n",
    "EI = mbti_weibo_data.iloc[:,47]\n",
    "NS = mbti_weibo_data.iloc[:,48]\n",
    "TF = mbti_weibo_data.iloc[:,49]\n",
    "JP = mbti_weibo_data.iloc[:,50]\n",
    "MBTI = mbti_weibo_data.iloc[:,51]\n",
    "\n",
    "dataSet = []\n",
    "EI_baseFeatures = baseFeatures.join(EI)\n",
    "dataSet.append(EI_baseFeatures)\n",
    "\n",
    "NS_baseFeatures = baseFeatures.join(NS)\n",
    "dataSet.append(NS_baseFeatures)\n",
    "\n",
    "TF_baseFeatures = baseFeatures.join(TF)\n",
    "dataSet.append(TF_baseFeatures)\n",
    "\n",
    "JP_baseFeatures = baseFeatures.join(JP)\n",
    "dataSet.append(JP_baseFeatures)\n",
    "\n",
    "# MBTI_baseFeatures = baseFeatures.join(MBTI)\n",
    "# dataSet.append(MBTI_baseFeatures)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "outputs": [],
   "source": [
    "#特征选择\n",
    "fx = JP_baseFeatures.iloc[:,0:47]\n",
    "fy = JP_baseFeatures.iloc[:,47]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Single Tree:0.5184049079754601 Random Forest:0.4447852760736196\n",
      "Single Tree:0.4570552147239264 Random Forest:0.5061349693251533\n",
      "Single Tree:0.5368098159509203 Random Forest:0.5214723926380368\n",
      "1\n",
      "Single Tree:0.48466257668711654 Random Forest:0.48773006134969327\n",
      "Single Tree:0.5245398773006135 Random Forest:0.48773006134969327\n",
      "Single Tree:0.5429447852760736 Random Forest:0.4785276073619632\n",
      "2\n",
      "Single Tree:0.4938650306748466 Random Forest:0.5030674846625767\n",
      "Single Tree:0.4785276073619632 Random Forest:0.5245398773006135\n",
      "Single Tree:0.5122699386503068 Random Forest:0.5398773006134969\n",
      "3\n",
      "Single Tree:0.5214723926380368 Random Forest:0.4662576687116564\n",
      "Single Tree:0.5030674846625767 Random Forest:0.5429447852760736\n",
      "Single Tree:0.46932515337423314 Random Forest:0.5306748466257669\n",
      "4\n",
      "Single Tree:0.5153374233128835 Random Forest:0.49693251533742333\n",
      "Single Tree:0.5030674846625767 Random Forest:0.49079754601226994\n",
      "Single Tree:0.5674846625766872 Random Forest:0.5245398773006135\n",
      "5\n",
      "Single Tree:0.450920245398773 Random Forest:0.5122699386503068\n",
      "Single Tree:0.5 Random Forest:0.4815950920245399\n",
      "Single Tree:0.5061349693251533 Random Forest:0.49079754601226994\n",
      "6\n",
      "Single Tree:0.5368098159509203 Random Forest:0.5429447852760736\n",
      "Single Tree:0.5184049079754601 Random Forest:0.49079754601226994\n",
      "Single Tree:0.48773006134969327 Random Forest:0.5153374233128835\n",
      "7\n",
      "Single Tree:0.5030674846625767 Random Forest:0.5061349693251533\n",
      "Single Tree:0.4785276073619632 Random Forest:0.4815950920245399\n",
      "Single Tree:0.50920245398773 Random Forest:0.5429447852760736\n",
      "8\n",
      "Single Tree:0.5276073619631901 Random Forest:0.5306748466257669\n",
      "Single Tree:0.50920245398773 Random Forest:0.5\n",
      "Single Tree:0.49079754601226994 Random Forest:0.5061349693251533\n",
      "9\n",
      "Single Tree:0.49079754601226994 Random Forest:0.5184049079754601\n",
      "Single Tree:0.5030674846625767 Random Forest:0.4938650306748466\n",
      "Single Tree:0.48466257668711654 Random Forest:0.4815950920245399\n",
      "10\n",
      "Single Tree:0.4938650306748466 Random Forest:0.4171779141104294\n",
      "Single Tree:0.5030674846625767 Random Forest:0.5552147239263804\n",
      "Single Tree:0.5552147239263804 Random Forest:0.4662576687116564\n",
      "11\n",
      "Single Tree:0.49079754601226994 Random Forest:0.46319018404907975\n",
      "Single Tree:0.4815950920245399 Random Forest:0.50920245398773\n",
      "Single Tree:0.5184049079754601 Random Forest:0.5030674846625767\n",
      "12\n",
      "Single Tree:0.4754601226993865 Random Forest:0.4785276073619632\n",
      "Single Tree:0.5429447852760736 Random Forest:0.5030674846625767\n",
      "Single Tree:0.5061349693251533 Random Forest:0.5276073619631901\n",
      "13\n",
      "Single Tree:0.5214723926380368 Random Forest:0.5245398773006135\n",
      "Single Tree:0.5030674846625767 Random Forest:0.450920245398773\n",
      "Single Tree:0.549079754601227 Random Forest:0.48466257668711654\n",
      "14\n",
      "Single Tree:0.4938650306748466 Random Forest:0.5\n",
      "Single Tree:0.5337423312883436 Random Forest:0.558282208588957\n",
      "Single Tree:0.549079754601227 Random Forest:0.5122699386503068\n",
      "15\n",
      "Single Tree:0.5061349693251533 Random Forest:0.48773006134969327\n",
      "Single Tree:0.4938650306748466 Random Forest:0.49693251533742333\n",
      "Single Tree:0.50920245398773 Random Forest:0.5920245398773006\n",
      "16\n",
      "Single Tree:0.5245398773006135 Random Forest:0.4754601226993865\n",
      "Single Tree:0.5306748466257669 Random Forest:0.4601226993865031\n",
      "Single Tree:0.5368098159509203 Random Forest:0.5\n",
      "17\n",
      "Single Tree:0.4662576687116564 Random Forest:0.5736196319018405\n",
      "Single Tree:0.5398773006134969 Random Forest:0.5153374233128835\n",
      "Single Tree:0.5276073619631901 Random Forest:0.5184049079754601\n",
      "18\n",
      "Single Tree:0.5429447852760736 Random Forest:0.5030674846625767\n",
      "Single Tree:0.48466257668711654 Random Forest:0.5184049079754601\n",
      "Single Tree:0.5306748466257669 Random Forest:0.5030674846625767\n",
      "19\n",
      "Single Tree:0.48773006134969327 Random Forest:0.4723926380368098\n",
      "Single Tree:0.43558282208588955 Random Forest:0.5184049079754601\n",
      "Single Tree:0.5122699386503068 Random Forest:0.49693251533742333\n",
      "0.558282208588957\n"
     ]
    }
   ],
   "source": [
    "# import modules\n",
    "from sklearn.feature_selection import (SelectKBest, chi2, SelectPercentile, SelectFromModel, SequentialFeatureSelector, SequentialFeatureSelector)\n",
    "\n",
    "# select K best features\n",
    "bestrsf = 0\n",
    "for k in range(20):\n",
    "    print(k)\n",
    "    X_best = SelectKBest(chi2, k=10).fit_transform(fx,fy)\n",
    "\n",
    "    # number of best features\n",
    "    X_best.shape[1]\n",
    "\n",
    "    trainModelTest(fx.iloc[:,0:4],fy)\n",
    "\n",
    "    rsfs = trainModelTest(X_best,fy)\n",
    "    if rsfs >bestrsf:\n",
    "        bestrsf = rsfs\n",
    "    trainModelTest(fx.iloc[:,0:47],fy)\n",
    "print(bestrsf)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "outputs": [],
   "source": [
    "def trainModelTest(X,Y):\n",
    "    Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,Y,test_size=0.3)\n",
    "\n",
    "    # print(Ytest)\n",
    "    clf = DecisionTreeClassifier(random_state=0)\n",
    "    rfc = RandomForestClassifier(n_estimators=20, max_depth=4)\n",
    "\n",
    "    clf = clf.fit(Xtrain,Ytrain)\n",
    "    score_c = clf.score(Xtest,Ytest)\n",
    "\n",
    "    rfc = rfc.fit(Xtrain,Ytrain)\n",
    "    score_r = rfc.score(Xtest,Ytest)\n",
    "\n",
    "    print('Single Tree:{}'.format(score_c),'Random Forest:{}'.format(score_r))\n",
    "    return score_r"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1084 entries, 0 to 1083\n",
      "Data columns (total 27 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   性别      1084 non-null   int64\n",
      " 1   微博数     1084 non-null   int64\n",
      " 2   注册年限    1084 non-null   int64\n",
      " 3   TOP1    1084 non-null   int64\n",
      " 4   TOP2    1084 non-null   int64\n",
      " 5   TOP3    1084 non-null   int64\n",
      " 6   TOP4    1084 non-null   int64\n",
      " 7   TOP5    1084 non-null   int64\n",
      " 8   数码      1084 non-null   int64\n",
      " 9   时尚      1084 non-null   int64\n",
      " 10  美妆      1084 non-null   int64\n",
      " 11  搞笑幽默    1084 non-null   int64\n",
      " 12  历史      1084 non-null   int64\n",
      " 13  体育      1084 non-null   int64\n",
      " 14  人文艺术    1084 non-null   int64\n",
      " 15  美食      1084 non-null   int64\n",
      " 16  动物宠物    1084 non-null   int64\n",
      " 17  运动健身    1084 non-null   int64\n",
      " 18  科学科普    1084 non-null   int64\n",
      " 19  教育      1084 non-null   int64\n",
      " 20  房产家居    1084 non-null   int64\n",
      " 21  摄影拍照    1084 non-null   int64\n",
      " 22  社会时事    1084 non-null   int64\n",
      " 23  电影      1084 non-null   int64\n",
      " 24  综艺节目    1084 non-null   int64\n",
      " 25  健康医疗    1084 non-null   int64\n",
      " 26  EI      1084 non-null   int64\n",
      "dtypes: int64(27)\n",
      "memory usage: 228.8 KB\n",
      "None\n",
      "Single Tree:0.6809815950920245 Random Forest:0.6748466257668712\n",
      "Single Tree:0.5736196319018405 Random Forest:0.6441717791411042\n",
      "Single Tree:0.6104294478527608 Random Forest:0.6809815950920245\n",
      "0.6441717791411042\n"
     ]
    }
   ],
   "source": [
    "# EI_baseFeatures.corr().loc['EI'].plot(kind='barh',figsize=(4,10))\n",
    "testDF = EI_baseFeatures\n",
    "testY = 'EI'\n",
    "# drop uncorrelated numeric features (threshold <0.2)\n",
    "corr = abs(testDF.corr().loc[testY])\n",
    "# print(corr)\n",
    "corr = corr[corr<0.07]\n",
    "cols_to_drop = corr.index.to_list()\n",
    "# print(cols_to_drop)\n",
    "df = testDF.drop(cols_to_drop, axis=1)\n",
    "print(df.info())\n",
    "# print(df.iloc[:,5])\n",
    "# df.corr().loc[testY].plot(kind='barh',figsize=(4,10))\n",
    "trainModelTest(testDF.iloc[:,0:4],fy)\n",
    "rsfss = trainModelTest(df.iloc[:,0:3],fy)\n",
    "trainModelTest(fx.iloc[:,0:47],fy)\n",
    "print(rsfss)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(-0.439, '粉丝数'), (-0.492, '视频累积播放量'), (-0.494, 'TOP3'), (-0.503, '互动数'), (-0.505, '微博数'), (-0.506, '美妆'), (-0.509, '时尚'), (-0.511, 'TOP2'), (-0.515, '注册年限'), (-0.516, '游戏'), (-0.519, '运动健身'), (-0.519, '综艺节目'), (-0.522, 'TOP4'), (-0.523, '教育'), (-0.524, '数码'), (-0.524, '房产家居'), (-0.525, '体育'), (-0.526, '电影'), (-0.528, '法律'), (-0.529, '母婴育儿'), (-0.529, '摄影拍照'), (-0.529, '宗教'), (-0.53, '电视剧'), (-0.53, 'TOP1'), (-0.531, '社会时事'), (-0.531, '旅游出行'), (-0.532, '性别'), (-0.532, '动物宠物'), (-0.532, '关注数'), (-0.533, '财经'), (-0.533, '科学科普'), (-0.533, '情感生活'), (-0.533, '健康医疗'), (-0.533, 'TOP5'), (-0.535, '读书作家'), (-0.535, '搞笑幽默'), (-0.536, '历史'), (-0.536, '公益'), (-0.537, '汽车'), (-0.537, '人文艺术'), (-0.538, '动漫'), (-0.54, '娱乐明星'), (-0.542, '美食'), (-0.542, '军事'), (-0.544, '颜值'), (-0.544, '音乐演出'), (-0.546, '互联网')]\n"
     ]
    }
   ],
   "source": [
    "#嵌入法\n",
    "from sklearn.model_selection import cross_val_score, ShuffleSplit\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "names = ['粉丝数','关注数','性别','注册年限','微博数','互动数','视频累积播放量','TOP1','TOP2','TOP3','TOP4','TOP5','数码','母婴育儿','时尚','美妆','颜值','搞笑幽默','历史','互联网','体育','人文艺术','公益','动漫','美食','情感生活','动物宠物','运动健身','科学科普','游戏','教育','音乐演出','军事','房产家居','汽车','宗教','摄影拍照','读书作家','社会时事','电影','娱乐明星','法律','综艺节目','旅游出行','财经','健康医疗','电视剧']\n",
    "X = fx\n",
    "Y = fy\n",
    "\n",
    "#n_estimators为森林中树木数量，max_depth树的最大深度\n",
    "rf = RandomForestRegressor(n_estimators=20, max_depth=4)\n",
    "scores = []\n",
    "for i in range(X.shape[1]):\n",
    "    #每次选择一个特征，进行交叉验证，训练集和测试集为7:3的比例进行分配，\n",
    "    #ShuffleSplit()函数用于随机抽样（数据集总数，迭代次数，test所占比例）\n",
    "    score = cross_val_score(rf, X.iloc[:,i:i+1], Y, scoring=\"r2\",cv=6)\n",
    "    scores.append((round(np.mean(score), 3), names[i]))\n",
    "\n",
    "#打印出各个特征所对应的得分\n",
    "print(sorted(scores, reverse=True))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['EI'], dtype='object')\n",
      "Single Tree:0.5269058295964125 Random Forest:0.6076233183856502\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['NS'], dtype='object')\n",
      "Single Tree:0.5672645739910314 Random Forest:0.5538116591928252\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['TF'], dtype='object')\n",
      "Single Tree:0.49551569506726456 Random Forest:0.5582959641255605\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['JP'], dtype='object')\n",
      "Single Tree:0.5134529147982063 Random Forest:0.5067264573991032\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模型训练\n",
    "\n",
    "#sklearn中所有的特征和标签都是分开导入的\n",
    "for data in dataSet:\n",
    "\n",
    "    print(pd.DataFrame(data.iloc[0:1,47]).columns)\n",
    "    # print(data.iloc[987:990,47].values.tolist())\n",
    "    # print(data.iloc[987:990,0:47].values.tolist())\n",
    "    # print(pd.DataFrame(data.iloc[0:1,47]).info())\n",
    "    Xtrain, Xtest, Ytrain, Ytest = train_test_split(data.iloc[:,0:47].values.tolist()\n",
    "                                                    ,data.iloc[:,47].values.tolist()\n",
    "                                                    ,test_size=0.3)\n",
    "    # print(Ytest)\n",
    "    clf = DecisionTreeClassifier(random_state=0)\n",
    "    rfc = RandomForestClassifier(random_state=0)\n",
    "\n",
    "    clf = clf.fit(Xtrain,Ytrain)\n",
    "    score_c = clf.score(Xtest,Ytest)\n",
    "\n",
    "    rfc = rfc.fit(Xtrain,Ytrain)\n",
    "    score_r = rfc.score(Xtest,Ytest)\n",
    "\n",
    "    print('Single Tree:{}'.format(score_c),'Random Forest:{}'.format(score_r))\n",
    "\n",
    "    rfc = RandomForestClassifier(n_estimators = 25)\n",
    "    rfc_s = cross_val_score(rfc, data.iloc[:,0:47].values.tolist()\n",
    "                                                    ,data.iloc[:,47].values.tolist(),cv=10)\n",
    "\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf_s = cross_val_score(clf, data.iloc[:,0:47].values.tolist()\n",
    "                                                    ,data.iloc[:,47].values.tolist(),cv=10)\n",
    "\n",
    "    plt.plot(range(1,11),rfc_s,label = 'RandomForest')\n",
    "    plt.plot(range(1,11),clf_s,label = 'DecitionTree')\n",
    "    plt.legend()\n",
    "    plt.show()"
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